计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 304-311.doi: 10.11896/jsjkx.210100157
张海波, 张益峰, 刘开健
ZHANG Hai-bo, ZHANG Yi-feng, LIU Kai-jian
摘要： 在移动边缘计算(MEC)与非正交多路接入(NOMA)技术相结合的车联网系统中,针对用户处理计算密集型和时延敏感型任务时面临的高时延问题,提出了一种基于博弈论和Q学习的任务卸载、迁移与缓存优化策略。首先,对基于NOMA-MEC的车联网任务卸载时延、迁移时延与缓存时延进行建模;其次,采用合作博弈算法获得最优用户分组,以实现卸载时延优化;最后,为避免出现局部最优,通过Q学习算法优化用户分组中的迁移缓存联合时延。仿真结果表明,所提方案相比对比方案,能有效提升卸载效率并降低约22%~43%的任务时延。
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